| 11 Dec 2024
Impact of clouds on vegetation albedo quantified by coupling an atmosphere and a vegetation radiative transfer model
Abstract. This paper investigates the influence of clouds on vegetation albedo. For this purpose, we use coupled atmosphere-vegetation radiative transfer (RT) simulations combining the library for Radiative transfer (libRadtran) and the vegetation Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE2.0) model. Both models are iteratively linked to more realistically simulate cloud–vegetation-radiation interactions above three types of canopies represented by the spherical, erectophile, and planophile leaf angle distributions. The coupled models are applied to simulate solar, spectral and broadband irradiances under cloud-free and cloudy conditions, with the focus on the visible to near-infrared wavelength range from 0.4 to 2.4 µm wavelengths. The simulated irradiances are used to investigate the spectral and broadband effect of clouds on the vegetation albedo. It is found that changes in solar zenith angle and cloud optical thickness are equally important for variations in the vegetation albedo. For solar zenith angles less than 50° –60°, the vegetation albedo is increased by clouds by up to 0.1. The greatest increase in albedo was observed during the transition from cloud-free to cloud conditions with a cloud optical thickness (τ ) of about 6. For larger values of τ the vegetation albedo saturates and increases only slightly. The increase of the vegetation albedo is a result of three effects: (i) dependence of the canopy reflectivity on the direct and diffuse fraction of downward irradiance, (ii) the shift in the spectral weighting of downward irradiance due to scattering and absorption by clouds, and (iii) multiple scattering between the top of canopy and the cloud base. The observed change in vegetation albedo due to cloudiness is parameterized by a polynomial function, representing a potential method to include cloud–vegetation-radiation interactions in numerical weather prediction and global climate models.
